The present disclosure relates generally to baysian networks, and more specifically, to a framework for learning a simple Bayesian network from data in parallel in a massively parallel processing (MPP) architecture.
Bayesian networks are probabilistic graphical models representing a set of random variables and their conditional dependencies. The variables of the Bayesian network can be continuous or discrete. In general a Bayesian network is found that fits best to given data, i.e. to learn the desired Bayesian network from given data. This learning process is accomplished by massively parallel data processing being performed by a large number of processors, such as hundreds or thousands, which conduct a set of coordinated computations in parallel. The processors may be arranged in a MPP architecture.